The information visualizer, an information workspace
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM SIGPLAN Notices
Memory controller policies for DRAM power management
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Hardware and Software Techniques for Controlling DRAM Power Modes
IEEE Transactions on Computers
Scheduler-based DRAM energy management
Proceedings of the 39th annual Design Automation Conference
An integrated approach to reducing power dissipation in memory hierarchies
CASES '02 Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems
Improving energy efficiency by making DRAM less randomly accessed
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Performance directed energy management for main memory and disks
ACM Transactions on Storage (TOS)
Design and implementation of power-aware virtual memory
ATEC '03 Proceedings of the annual conference on USENIX Annual Technical Conference
PABC: Power-Aware Buffer Cache Management for Low Power Consumption
IEEE Transactions on Computers
Interaction-aware energy management for wireless network cards
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Response time in man-computer conversational transactions
AFIPS '68 (Fall, part I) Proceedings of the December 9-11, 1968, fall joint computer conference, part I
Software–hardware cooperative power management for main memory
PACS'04 Proceedings of the 4th international conference on Power-Aware Computer Systems
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Energy efficiency has become one of the most important factors in the development of computer systems. As applications become more data centric and put more pressure on the memory subsystem, managing energy consumption of main memory is becoming critical. Therefore, it is critical to take advantage of all memory idle times by placing memory in low power modes even during the active process execution. However, current solutions only offer energy optimizations on a perprocess basis and are unable to take advantage of memory idle times when the process is executing. To allow accurate and fine-grained memory management during the process execution, we propose Interaction-Aware Memory Energy Management (IAMEM). IAMEM relies on accurate correlation of user-initiated tasks with the demand placed on the memory subsystem to accurately predict power state transitions for maximal energy savings while minimizing the impact on performance. Through detailed trace-driven simulation, we show that IAMEM reduces the memory energy consumption by as much as 16% as compared to the state-of-the-art approaches, while maintaining the user-perceivable performance comparable to the system without any energy optimizations.